Monocular 3D object detection has become a mainstream approach in automatic
driving for its easy application. A prominent advantage is that it does not
need LiDAR point clouds during the inference. However, most current methods
still rely on 3D point cloud data for labeling the ground truths used in the
training phase. This inconsistency between the training and inference makes it
hard to utilize the large-scale feedback data and increases the data collection
expenses. To bridge this gap, we propose a new weakly supervised monocular 3D
objection detection method, which can train the model with only 2D labels
marked on images. To be specific, we explore three types of consistency in this
task, i.e. the projection, multi-view and direction consistency, and design a
weakly-supervised architecture based on these consistencies. Moreover, we
propose a new 2D direction labeling method in this task to guide the model for
accurate rotation direction prediction. Experiments show that our
weakly-supervised method achieves comparable performance with some fully
supervised methods. When used as a pre-training method, our model can
significantly outperform the corresponding fully-supervised baseline with only
1/3 3D labels. this https URL

本文提出了一种基于弱监督学习的单目 3D 物体检测方法，该方法只使用图像上标记的 2D 标签进行模型训练，并探索了投影、多视图和方向这三种不同类型的一致性约束以及设计了一种新的 2D 方向标记方法用于准确的旋转方向预测。实验证明，该方法在一些完全监督方法上具有可比较的性能，并且当用作预训练方法时，仅使用 1 / 3 3D 标签就可以显着优于对应的完全监督基线。